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# Mahotas – Zernike Moments

• Last Updated : 30 Jun, 2021

In this article we will see how we can get the zernike moments of the given image in mahotas. Zernike polynomials are an orthogonal basis set (a set of functions for which the integral of the product of any pair of functions is zero). In image processing, computer vision and related fields, an image moment is a certain particular weighted average of the image pixelsâ€™ intensities, or a function of such moments, usually chosen to have some attractive property or interpretation. Image moments are useful to describe objects after segmentation.
For this tutorial we will use ‘lena’ image, below is the command to load the lena image

Below is the lena image

In order to do this we will use mahotas.features.zernike_moments method
Argument : It takes image object and integer as argument
Return : It returns 1-D array

Note : Input image should be filtered or should be loaded as grey
In order to filter the image we will take the image object which is numpy.ndarray and filter it with the help of indexing, below is the command to do this

image = image[:, :, 0]

Below is the implementation

## Python3

 # importing required librariesimport mahotasimport mahotas.demosfrom pylab import gray, imshow, showimport numpy as npimport matplotlib.pyplot as plt   # loading imageimg = mahotas.demos.load('lena')   # filtering imageimg = img.max(2) print("Image")   # showing imageimshow(img)show() # radiusradius = 10 # computing zernike momentsvalue = mahotas.features.zernike_moments(img, radius)   # printing valueprint(value)

Output :

Image

[0.31830989 0.01261485 0.00614926 0.00769591 0.0097145  0.01757332
0.00617458 0.01008905 0.01415304 0.01099679 0.02894761 0.01838737
0.0074247  0.01333135 0.01958184 0.00431827 0.00540781 0.01675913
0.03511082 0.00699177 0.00357231 0.01593838 0.01621848 0.0240565
0.0154929 ]

Another example

## Python3

 # importing required librariesimport mahotasimport numpy as npfrom pylab import gray, imshow, showimport osimport matplotlib.pyplot as plt  # loading imageimg = mahotas.imread('dog_image.png')  # filtering imageimg = img[:, :, 0]   print("Image")   # showing imageimshow(img)show() # radiusradius = 10 # computing zernike momentsvalue = mahotas.features.zernike_moments(img, radius)   # printing valueprint(value)

Output :

Image

[0.31830989 0.00985427 0.00714652 0.00171408 0.00442245 0.01796711
0.00716781 0.00179965 0.0039829  0.0031081  0.02447476 0.0011686
0.009291   0.00174885 0.00357579 0.00692029 0.0043969  0.03528869
0.00264739 0.01381883 0.00750501 0.0036528  0.00867514 0.01298398
0.0129556 ]

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